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In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle unoriented edges or latent assumptions violation suffered by conventional methods, researchers formulated a reinforcement learning (RL) procedure for causal discovery and equipped a REINFORCE algorithm to search for the best rewarded directed acyclic graph. The two keys to the overall performance of the procedure are the robustness of RL methods and the efficient encoding of variables. However, on the one hand, REINFORCE is prone to local convergence and unstable performance during training. Neither trust region policy optimization, being computationally expensive, nor proximal policy optimization (PPO), suffering from aggregate constraint deviation, is a decent alternative for combinatory optimization problems with considerable individual subactions. We propose a trust region-navigated clipping policy optimization method for causal discovery that guarantees both better search efficiency and steadiness in policy optimization, in comparison with REINFORCE, PPO, and our prioritized sampling-guided REINFORCE implementation. On the other hand, to boost the efficient encoding of variables, we propose a refined graph attention encoder called SDGAT that can grasp more feature information without priori neighborhood information. With these improvements, the proposed method outperforms the former RL method in both synthetic and benchmark datasets in terms of output results and optimization robustness.

Workload characteristics are vital for both data center operation and job scheduling in co-located data centers, where online services and batch jobs are deployed on the same production cluster. In this paper, a comprehensive analysis is conducted on Alibaba‘s cluster-trace-v2018 of a production cluster of 4034 machines. The findings and insights are the following: (1) The workload on the production cluster poses a daily cyclical fluctuation, in terms of CPU and disk I/O utilization, and the memory system has become the performance bottleneck of a co-located cluster. (2) Batch jobs including their tasks and derived instances can be approximated as Zipf distribution. However, for all batch jobs with directed acyclic graph dependency, they suffer from co-location with online services since the online services are highly prioritized. (3) The resource usages of containers have similar cyclical fluctuation consistent with the whole cluster, while their memory usages remain approximately constant. (4) The number of batch jobs co-located with online services is dependent on the mispredictions per kilo instructions of online services. In order to guarantee the QoS of online services, when the MPKI of online services rises, the number of batch jobs to be co-located on the same machine should decrease.

With the rapid development of Internet of Things (IoT), digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of digital twin in IoT. In this paper, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through access point (AP) to help resource limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging Directed Acyclic Graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.

The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks' risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.

The application placement strategies in the hierarchical fog-cloud environment based on the directed acyclic graph (DAG) for rapid execution is an NP-hard optimization problem. Although heuristics have been proposed to generate sub-optimal solutions based on non-preemptive placement policy, merging multiple DAG-based applications into one application is simple but ineffective for minimizing the overall makespan due to the lack of consideration of fairness among multiple DAG-based applications. We design an application placement strategy based on dynamic scheduling that can effectively utilize the schedule gaps in the virtual machines of the different layers to minimize the makespan that meets the deadlines.

Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base (BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base (C-BRB) model based on Directed Acyclic Graph (DAG) structure is proposed in this paper. By dispersing numerous attributes into the parallel structure composed of different sub-BRBs, C-BRB can effectively reduce the amount of calculation with acceptable result. At the same time, a path selection strategy considering the accuracy of child nodes is designed in C-BRB to obtain the most suitable sub-models. Finally, a fusion method based on Evidential Reasoning (ER) rule is used to combine the belief rules of C-BRB and generate the final results. To illustrate the effectiveness and reliability of the proposed method, a case study of fault diagnosis of rolling bearing is conducted, and the result is compared with other methods.

Recently, neural architecture search (NAS) has gained a lot of attention as a tool for constructing deep neural networks automatically. NAS methods have successfully found convolutional neural networks (CNNs) that exceed human expert-designed networks on image classification in computer vision. However, there are growing demands for semantic segmentation in several areas including remote sensing image analysis. In this paper, we introduce an evolutionary NAS method for semantic segmentation of high-resolution aerial images. The proposed method leverages the complementary strengths of gene expression programming and cellular encoding to develop an encoding scheme, called symbolic linear generative encoding (SLGE), for evolving cells (directed acyclic graphs) as building-blocks to construct modularized encoder-decoder CNNs via an evolutionary process. SLGE can evolve cells with multi-branch and shortcut connections similar to the Inception-ResNet-like modules which can improve training and inference performance in deep neural networks. In experiments, we demonstrate the effectiveness of the proposed method on the challenging ISPRS Vaihingen, Potsdam and UAVid semantic segmentation benchmarks. Compared with recent state-of-the-art systems, our network, dubbed SLGENet, improves the overall accuracy performance on Vaihingen and Potsdam; and achieves a competitive overall accuracy on UAVid using fewer parameters. Our method achieves promising results in a little time of 2.5 GPU days.

Bayesian Network (BN) modeling is a prominent and increasingly popular computational systems biology method. It aims to construct probabilistic networks from the large heterogeneous biological datasets that reflect the underlying networks of biological relationships. Currently, a variety of strategies exist for evaluating BN methodology performance, ranging from utilizing artificial benchmark datasets and models, to specialized biological benchmark datasets, to simulation studies that generate synthetic data from predefined network models. The latter is arguably the most comprehensive approach; however, existing implementations are typically limited by their reliance on the SEM (structural equation modeling) framework, which includes many explicit and implicit assumptions that may be unrealistic in a typical biological data analysis scenario. In this study, we develop an alternative, purely probabilistic, simulation framework that more appropriately fits with real biological data and biological network models. In conjunction, we also expand on our current understanding of the theoretical notions of causality and dependence / conditional independence in BNs and the Markov Blankets within.

Motivated by a multimodal neuroimaging study for Alzheimer's disease, in this article, we study the inference problem, i.e., hypothesis testing, of sequential mediation analysis. The existing sequential mediation solutions mostly focus on sparse estimation, while hypothesis testing is an utterly different and more challenging problem. Meanwhile, the few mediation testing solutions often ignore the potential dependency among the mediators, or cannot be applied to the sequential problem directly. We propose a statistical inference procedure to test mediation pathways when there are sequentially ordered multiple data modalities and each modality involves multiple mediators. We allow the mediators to be conditionally dependent, and the number of mediators within each modality to diverge with the sample size. We produce the explicit significance quantification and establish the theoretical guarantees in terms of asymptotic size, power, and false discovery control. We demonstrate the efficacy of the method through both simulations and an application to a multimodal neuroimaging pathway analysis of Alzheimer's disease.

Bayesian network classifiers (BNCs) are powerful tools for graphically encoding the dependency relationships among variables in a directed acyclic graph and reasoning under conditions of uncertainty. Ever increasing data quantity makes ever more urgent the need of BNCs that are highly scalable and can perform significantly better in terms of classification. Numerous approaches have been proposed to mine conditional dependencies among attributes implicated in labeled training data under the framework of supervised learning, whereas the specific characteristics of unlabeled testing instances receive less attention. That may lead to overfitting and degradation in classification performance. In this paper, we argue that the knowledge learned from labeled training dataset and that from unlabeled testing instance are complementary in nature. The testing instance is pre-assigned with any possible label to make it complete, then log-likelihood function is introduced and redefined to measure the extents to which the learned BNC fits training or testing data. Heuristic search strategy is applied to learn two kinds of arbitrary k-dependence BNCs (general BNC for modeling training dataset and local BNC for modeling testing instance), which will work as an ensemble to make the final prediction under the framework of semi-supervised learning. The experimental evaluation on 40 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves competitive classification performance compared with state-of-the-art BNCs and their variants, such as CFWNB, WATAN, FKDB, SKDB and IWAODE.

With the increasing demand for distributed big data analytics and data-intensive programs which contribute to large volumes of packets among processing elements (PEs) and memory banks, we witness a pressing need for new mathematical models and algorithms that can engineer a brain-inspired plasticity into the computing platforms by mining the topological complexity of high-level programs (HLPs) and exploiting their self-similar and fractal characteristics for designing reconfigurable domain-specific computing architectures. In this article, we present Plasticity-on-Chip (PoC) by engineering plasticity into ”artificial brains” to mine and exploit the self-similarity of HLPs. First, we present a communication modeling of HLPs (e.g., C/C++ implementations of various applications) that relies on static and dynamic compiler analysis of programs with varying input seeds, performing comprehensive program analysis of all traces, and representing the HLPs as weighted directed acyclic graphs while capturing the intrinsic timing constraints and data/control flow requirements. Second, we propose a rigorous mathematical framework for determining the optimal parallel degree of executing a set of interacting HLPs (by partitioning them into clusters of densely interconnected supernodes - tasks) which helps us decide the number of available heterogeneous PEs, the amount of required memory and the structure of the synthesized deadlock-free irregular NoC topology that offers an efficient communication medium. These clusters serve as abstract models of computation for the synthesized PEs within the parallel execution model. Finally, exploiting the fractal and complex networks concepts, we extract in-depth features from graphs that serve as inputs for distributed reinforcement learning. Our experimental results on synthesized PEs and NoCs show performance improvements as high as 7.61x when compared to the traditional NoC and 2.6x compared to gem5-Aladdin.

Directed acyclic graphs (DAGs) are increasingly used in epidemiology to identify and address different types of bias. The present work aims to demonstrate how DAG-based data simulation can be used to understand bias and compare data analytical strategies in an educational context. Examples based on classical confounding situations and an M-DAG are examined and used to introduce basic concepts and demonstrate some important features of regression analysis, as well as the harmful effect of adjusting for a collider variable. Other potential uses of DAG-based data simulation include systematic comparisons of data analytical strategies or the evaluation of the role of uncertainties in a hypothesized DAG structure, including other types of bias such as information bias. DAG-based data simulations, like those presented here, should facilitate the exploration of several key epidemiological concepts, DAG theory and data analysis. Some suggestions are also made on how to further expand the ideas from this study.

We present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.

The performance of deep neural networks is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (EvoNAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, EvoNAS suffers from extremely high computational costs because a large number of performance evaluations are usually required in evolutionary optimization, and training deep neural networks is itself computationally very expensive. To address this issue, this article proposes a computationally efficient framework for the evolutionary search of convolutional networks based on a directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted so that the fitness of all offspring individuals can be evaluated without training them. Finally, we encode a channel attention mechanism in the search space to enhance the feature processing capability of the evolved neural networks. We evaluate the proposed algorithm on the widely used datasets, in comparison with 30 state-of-the-art peer algorithms. Our experimental results show that the proposed algorithm is not only computationally much more efficient but also highly competitive in learning performance.

The employment of directed acyclic graphs to advance the tracking, control and appraisal of crystallographic phasing strategies is discussed., Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.

Dynamic dataflow scheduling enables effective exploitation of concurrency while making parallel programming easier. To this end, analyzing the inherent degree of concurrency available in dataflow graphs is an important task, since it may aid compilers or programmers to assess the potential performance a program can achieve via parallel execution. However, traditional concurrency analysis techniques only work for DAGs (directed acyclic graphs), hence the need for new techniques that contemplate graphs with cycles. In this paper we present techniques to perform concurrency analysis on generic dynamic dataflow graphs, even in the presence of cycles. In a dataflow graph, nodes represent instructions and edges describe dependencies. The novelty of our approach is that we allow concurrency between different iterations of the loops. Consequently, a set of concurrent nodes may contain instructions from different loops that can be proven independent. In this work, we provide a set of theoretical tools for obtaining bounds and illustrate implementation of parallel dataflow runtime on a set of representative graphs for important classes of benchmarks to compare measured performance against derived bounds.

Connected vehicles are set to define the future of transportation; however, this upcoming technology continues to be plagued with serious security risks. If these risks are not addressed in a timely fashion, then they could threaten the adoption and success of this promising technology. This article deals with a specific class of attacks in connected vehicles, namely tampering attacks caused due to compromise of on-board sensors. Current centralized solutions that employ trusted infrastructure to protect against adversarial manipulation of information cannot validate the correctness of the shared data and do not scale well. To overcome these issues, decentralized protection mechanisms by means of blockchain technology have emerged as a promising research direction. However, current permission-less, linear blockchain-based solutions have low transaction performance and high computational cost, thereby making it difficult to adopt them for security in connected vehicles. In this article, we present TangleCV, a directed acyclic graph–based distributed ledger technique for connected vehicles to address data tampering threats in connected vehicular networks. We describe new validation steps, tip selection strategies, and cumulative weight definition for TangleCV that not only meets the timing constraints of the connected vehicular networks but also secures the network against threats due to tampering attacks. We describe how the reputation of the network is established in TangleCV using trust factors calculated on the basis of ability, integrity, and benevolence of the nodes in the network. We present numerical results that demonstrate that the average value of the time to first approval decreases by more than 70% as the network evolves from a low load to a high load in the case of the nearest neighbor strategy. We observe that more than 60% of the nodes are approved in a low-load network and this number increases to 80% in a high-load network for the nearest neighbor strategy. The standard deviation of error measurements for nodes experiencing tampering attack is around 60% higher as compared to nodes that do not experience such an attack.

Blockchain technology has emerged as a novel distributed ledger technology, facilitating data sharing and system management securely and efficiently without interventions from a central authority. However, blockchain technology alone is not suitable for enterprise-class applications, mainly due to the limitations in capacity expansion and verification speed of blockchain systems. This paper proposes a secure and effective construction scheme for blockchain networks to improve performance and address the effective management concerns of blockchain data based on transaction categories. We designed a network link protocol to construct a directed acyclic graph (DAG) blockchain network and used a sharding protocol to divide the DAG blockchain into multiple category shards to process transactions in parallel. We then extensively evaluated our proposed design on local clusters. The experimental results show that our link and shard protocols achieved high throughput and the category-based sharded DAG blockchain demonstrated high scalability.

meaning representations (AMRs) represent sentence semantics as rooted labeled directed acyclic graphs. Though there is a strong correlation between the AMR graph of a sentence and its corresponding dependency tree, the recent neural network AMR parsers do neglect the exploitation of dependency structure information. In this paper, we explore a novel approach to exploiting dependency structures for AMR parsing. Unlike traditional pipeline models, we treat dependency parsing as an auxiliary task for AMR parsing under the multi-task learning framework by sharing neural network parameters and selectively extracting syntactic representation by the attention mechanism. Particularly, to balance the gradients and focus on the AMR parsing task, we present a new dynamical weighting scheme in the loss function. The experimental results on the LDC2015E86 and LDC2017T10 dataset show that our dependency-auxiliary AMR parser significantly outperforms the baseline and its pipeline counterpart, and demonstrate that the neural AMR parsers can be greatly boosted with the help of effective methods of integrating syntax.

E-commerce platforms categorize their products into a multi-level taxonomy tree with thousands of leaf categories. Conventional methods for product categorization are typically based on machine learning classification algorithms. These algorithms take product information as input (e.g., titles and descriptions) to classify a product into a leaf category. In this article, we propose a new paradigm based on machine translation . In our approach, we translate a product’s natural language description into a sequence of tokens representing a root-to-leaf path in a product taxonomy. In our experiments on two large real-world datasets, we show that our approach achieves better predictive accuracy than a state-of-the-art classification system for product categorization. In addition, we demonstrate that our machine translation models can propose meaningful new paths between previously unconnected nodes in a taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph. We discuss how the resultant taxonomy directed acyclic graph promotes user-friendly navigation, and how it is more adaptable to new products.

Directed graphs are widely used to model data flow and execution dependencies in streaming applications. This enables the utilization of graph partitioning algorithms for the problem of parallelizing execution on multiprocessor architectures under hardware resource constraints. However due to program memory restrictions in embedded multiprocessor systems, applications need to be divided into parts without cyclic dependencies. We found that this can be done by a subsequent second graph partitioning step with an additional acyclicity constraint. We have four main contributions. First, we show that this more constrained version of the graph partitioning problem is NP-complete and present linear time heuristics. We then integrate them into an existing multi-level graph partitioning framework to better handle large graphs. This achieves a 9% reduction of the edge cut compared to the previous single-level algorithm. Based on this, we engineer an evolutionary algorithm to further reduce the cut, achieving a 30% reduction on average compared to the state of the art. Finally, we integrate the partitioning heuristics into a graph compiler for an embedded multiprocessor architecture and show that this can reduce the amount of communication for a real-world imaging application and thereby accelerate it by an average of 11%. It is shown that the compiler can emit optimized code for vastly different hardware platforms using the heuristics. In addition, we demonstrate how a custom fitness function for the evolutionary algorithm can be used to optimize other objectives like load balancing if the communication volume is not predominantly important on a given hardware platform.

The study of multidimensional well-being has long recognized the importance of formalizing the interaction between dimensions, but came short of treating this formally. In this paper, we show that the statistical technique of Bayesian Networks is an intuitive and powerful instrument that allows to model the dependence structure among the different dimension of well-being. Moreover, Bayesian Networks are useful to understand the effectiveness of policies directed to one or more dimensions, as well as to design more effective interventions to improve well-being. The new approach is illustrated with an empirical application for a selection of Western and Eastern European countries.

Like other emerging fields, Stream Processing Engines (SPEs) pose several challenges to the researchers e.g., resource awareness, dynamic configurations, heterogeneous clusters, load balancing, and topology awareness. All of these aspects play a major role in the job scheduling process. Currently, SPEs ignore topology’s structure while scheduling. Due to this, frequently communicating tasks may end up at different computing nodes which causes problems for achieving the maximum throughput. In this paper, TOP-Storm—a scheduler based on topology’s DAG (Directed Acyclic Graph) is proposed for Apache Storm (a popular open-source SPE) that optimize resource usage for heterogeneous clusters. The aim is to improve efficiency using resource-aware task assignments that results in enhanced throughput and optimize resource utilization. TOP-Storm is divided into two phases: In the first phase, executors are logically grouped with the help of DAG to minimize inter-group communication. In the second phase, these groups are assigned to physical nodes starting from the most powerful node. Results are generated with the help of two benchmark topologies and results are compared with two state-of-the-art scheduling algorithms. Experiment results show up to 39% and 11% improvement in throughput as compared to the default Apache Storm scheduler and R-Storm, respectively.

Basic knowledge of ideas of causal inference can help students to think beyond data, that is, to think more clearly about the data generating process. Especially for (maybe big) observational data, qualitative assumptions are important for the conclusions drawn and interpretation of the quantitative results. Concepts of causal inference can also help to overcome the mantra “Correlation does not imply Causation.” To motivate and introduce causal inference in introductory statistics or data science courses, we use simulated data and simple linear regression to show the effects of confounding and when one should or should not adjust for covariables.

Turning is the one of the most commonly available and least expensive machining operations. Confronted with the increased number of part variants and the requirement for fast system responsiveness in dynamic environments, traditional methods for building a computer-aided process planning (CAPP) system for turning are infeasible due to the fixed feature library and hard-coded heuristic rules. In this paper, a customizable approach for automatic process planning of rotational parts is proposed to boost the productivity of enterprises by realizing multi-level machining feature recognition and knowledge-based machining activity/resource selection. First, from the decomposed cells of the turning area, basic turning features are successively recognized based on a novel cell machinability analysis. The high-level custom features are define based on the directed acyclic graph and recognized from the basic feature precedence graph using the subgraph matching algorithm. To facilitate the knowledge-based process planning, a knowledge base is then established utilizing ontology, in which the taxonomies, properties, and causal relationships among the core concepts, namely, machining feature, machining operation, cutting tool, and machine tool, are formally defined. Finally, a five-step procedure is proposed to automatically infer manufacturing activity/resource for multi-level features through rule-based reasoning. The effectiveness and extensibility of the proposed approach are validated through two case studies on complex rotational parts.

Several disease affecting the human respiratory system, such as asthma, pneumonia, etc. are associated with distinctive sounds. Here, we propose a methodology towards their automatic identification by means of signal processing and pattern recognition algorithms. We designed a suitable feature set based on wavelet packet analysis characterizing data coming from diverse classes of respiratory sounds following the logic of the challenge organised within the International Conference on Biomedical Health Informatics in 2017. The patterns revealed by the feature extraction stage are modelled by hidden Markov models. Automatic identification is carried out via a directed acyclic graph (DAG) scheme limiting the problem space while based on decisions made by the available HMMs. Thorough experiments following a well-established protocol demonstrate the efficacy of the proposed solution. Indeed, such a DAG-based structure outperforms the current state of the art including deep networks based on convolutional kernels. Importantly, the presented solution offers a high level of explainability as one is able to investigate the effective DAG path and understand both correct and incorrect predictions.

Technological developments have taken place in many areas since the occurrence of industrial revolution. The technological developments have entered the digitalization path with the industry 4.0 and have taken the potential of firms in terms of volume and diversity in response to unexpected and sudden changes in demand and markets as well as efficiency and increase the competitiveness and technological advancements in the process of supplying them and accelerated the digitalization process. With all this, it has highlighted the structure of the digital supply chain agility (ADSC), which is a means of competitiveness in today's dynamic and turbulent business environment within the supply chain. At this stage, logistics companies have sought to integrate digital technologies, smart systems, internet, robots and mobile applications into the supply chain, thus aiming to achieve higher agility. Firms aim to improve their processes and to keep customer satisfaction high, to maintain their profitability and to survive in the competitive market. The aim of this study is to increase the ability to respond to the changing and developing market and market conditions and to include the best logistics company which can fulfill the demands and requirements of a company that is trying to comply with Industry 4.0 requirements. Intuitionistic fuzzy TOPSIS method is employed to select the most appropriate logistic company.

Moving towards comprehensive digitalization of production facilities, it is critical to know the location of work pieces, charges, or other objects of interest that change location over time during production. For the case of a limited traceability of these objects, we first present a theoretical approach that performs a recursive Bayesian estimation of the object’s location over time based on typical passage measurements in production (e. g. light barriers or RFID systems). The probabilistic method is based on a directed acyclic graph modeling the transfer and sojourn of the objects in the production network. Subsequently, the method is validated on simulated data while varying both size and measurement conditions of the process. The results show the benefit of the proposed method against a single estimation and demonstrate its potential for the application in real time scenarios.

This paper deals with the issue of geometry representation of voxelized three-dimensional scenes using hierarchical data structures. These include pointerless Sparse Voxel Octrees that have no pointers on child nodes and allow a compact binary representation. However, if necessary, there is a possibility to reconstruct these pointers for rapid traversing. Sparse Voxel Directed Acyclic Graphs added 32-bit pointers to child nodes and merging of common subtrees, which can be considered lossless compression. By merging common subtrees, no decompression overhead occurs at the time of traversing. The hierarchical data structure proposed herein - the Pointerless Sparse Voxel Directed Acyclic Graph - incorporates the benefits of both - pointerless Sparse Voxel Octrees (by avoiding storing pointers on child nodes) and Sparse Voxel Directed Acyclic Graphs (by allowing the merging of common subtrees due the introduction of labels and callers). The proposed data structure supports the quick and easy reconstruction of pointers by introducing the Active Child Node Count. It also potentially allows Child Node Mask compression of its nodes. This paper presents the proposed data structure and its binary-level encoding in detail. It compares the effectiveness of the representation of voxelized three-dimensional scenes (originally represented in OBJ format) in the proposed data structure with the data structures mentioned above. It also summarizes statistical data providing a more detailed description of the various parameters of the data structure for different scenes stored in multiple resolutions.

Secure multi-party computation (SMC) allows parties to jointly compute a function over their inputs, while keeping every input confidential. It has been extensively applied in tasks with privacy requirements, such as privacy-preserving data mining (PPDM), to learn task output and at the same time protect input data privacy. However, existing SMC-based solutions are ad-hoc – they are proposed for specific applications, and thus cannot be applied to other applications directly. To address this issue, we propose a privacy model $\mathsf {DAG}$ DAG (Directed Acyclic Graph) that consists of a set of fundamental secure operators (e.g., +, -, ×, /, and power). Our model is general – its operators, if pipelined together, can implement various functions, even complicated ones like Naive Bayes classifier. It is also extendable – new secure operators can be defined to expand the functions that the model supports. For case study, we have applied our $\mathsf {DAG}$ DAG model to two data mining tasks: kernel regression and Naive Bayes. Experimental results show that $\mathsf {DAG}$ DAG generates outputs that are almost the same as those by non-private setting, where multiple parties simply disclose their data. The experimental results also show that our $\mathsf {DAG}$ DAG model runs in acceptable time, e.g., in kernel regression, when training data size is 683,093, one prediction in non-private setting takes 5.93 sec, and that by our $\mathsf {DAG}$ DAG model takes 12.38 sec.

Recent researches try to integrate word information into the character-based Chinese NER by modifying the structure of the standard BiLSTM-CRF model. They follow the paradigm of explicitly modeling forward and backward sequences, adopting an LSTM variant that takes both characters and words as input for each direction. Though enriching the representations, these models cannot fully exploit the interaction between future and past contexts. In this paper, we propose a novel word-character graph convolution network (WC-GCN) which uses a cross GCN block to simultaneously process the word-character directed acyclic graphs (DAGs) of two directions. To improve the capture of long-distance dependency, a global attention GCN block is introduced to learn node representations conditioned on a global context. In both blocks, unlike previous works where each word is attached to its associated character or taken as a shortcut between LSTM cells, words and characters are treated equally as nodes in the graph and have their instance-specific representations. Experiments on four widely used datasets show that our proposed model can work standalone or with the standard BiLSTM. Both forms can outperform previous LSTM-based models without training on extra corpora while only an external lexicon and its corresponding pretrained character and word embeddings are needed.

This paper provides a rich framework to estimate the causal relationship among eighteen features (related to the product type and classification) on an agronomy study by using Bayesian Networks, which are a type of probabilistic graphical model. Thereby, with this class of models, we aimed to classify and identify the complaints based on corn seed commercialization. Simulation studies were used to compare both adopted algorithms, K2 and PC, and their hybrid version. These studies indicate excellent classification performance, given the knowledge of the network structure. After the estimated Directed Acyclic Graph, three features (Brand, Germination percentage, and Amount of commercialized bags) were evidenced as Impacting factors in the complaints based on corn seed commercialization.

Detecting the source of an outbreak cluster during a pandemic like COVID-19 can provide insights into the transmission process, associated risk factors, and help contain the spread. In this work we study the problem of source detection from multiple snapshots of spreading on an arbitrary network structure. We use a spatial temporal graph convolutional network based model (SD-STGCN) to produce a source probability distribution, by fusing information from temporal and topological spaces. We perform extensive experiments using popular compartmental simulation models over synthetic networks and empirical contact networks. We also demonstrate the applicability of our approach with real COVID-19 case data.

Random number generators (RNG) are an underlying part of Proof-of-Stake consensus protocols and are critically important for many distributed applications on blockchains and directed acyclic graphs (DAGs). A fault-tolerant approach needs the communication and computation synchronicity assumptions to resolve it. The authors present DisCO-an upgraded version of the No-Dealer algorithm. DisCO works under the partial synchronous assumption and guarantees output per run, compared to No-Dealer, which works in a synchronous model and either generates output or detects a faulty participant. DisCO's communication model is more practical than NoDealer's as it meets the Internet. We implemented DisCO as a decentralized application on Hedera Hashgraph DAG. The source code and performance tests are provided.

Workflow engines are commonly used to orchestrate large-scale scientific computations such as, but not limited to weather, climate, natural disasters, food safety, and territorial management. However, to implement, manage, and execute real-world scientific applications in the form of workflows on multiple infrastructures (servers, clusters, cloud) remains a challenge. In this paper, we present DagOnStar ( D irected A cyclic G raph On Anything ), a lightweight Python library implementing a workflow paradigm based on parallel patterns that can be executed on any combination of local machines, on-premise high performance computing clusters, containers, and cloud-based virtual infrastructures. DagOnStar is designed to minimize data movement to reduce the application storage footprint. A case study based on a real-world application is explored to illustrate the use of this novel workflow engine: a containerized weather data collection application deployed on multiple infrastructures. An experimental comparison with other state-of-the-art workflow engines shows that DagOnStar can run workflows on multiple types of infrastructure with an improvement of 50.19% in run time when using a parallel pattern with eight task-level workers.

BACKGROUND: Integrating results from multiple samples is often desirable, but privacy restrictions may preclude full data pooling, and most datasets do not include fully harmonized variable sets. We propose a simulation-based method leveraging partial information across datasets to guide creation of synthetic data, based on explicit assumptions about the underlying causal structure, that permits pooled analyses that adjust for all desired confounders in the context of privacy restrictions. METHODS: This proof-of-concept project uses data from the Health and Retirement Study (HRS) and Atherosclerosis Risk in Communities (ARIC) study. We specified an estimand of interest and a directed acyclic graph (DAG) summarizing the presumed causal structure for the effect of glycated hemoglobin (HbA1c) on cognitive change. We derived publicly reportable statistics to describe the joint distribution of each variable in our DAG. These summary estimates were used as data-generating rules to create synthetic datasets. After pooling, we imputed missing covariates in the synthetic datasets and used the synthetic data to estimate the pooled effect of HbA1c on cognitive change, adjusting for all desired covariates. RESULTS: Distributions of covariates, as well as model coefficients and associated standard errors for our model estimating the effect of HbA1c on cognitive change were similar across cohort-specific original and pre-imputation synthetic data. The estimate from the pooled synthetic incorporates control for confounders measured in either original dataset. DISCUSSION: Our approach has advantages over meta-analysis or individual-level pooling/data harmonization when privacy concerns preclude data sharing and key confounders are not uniformly measured across datasets.

With the rapid development of multimedia broadcasting and communication technology, multimedia services in the form of componentized network applications deployed in the ubiquitous computing environments comprised of cloud-edge-end 3 tiers of nodes are coming true. However, the existing works usually assume the application is non-partitioned or the computing environment with multiple resources is strictly hierarchical. In this work, we characterize interconnected function components in a multimedia service with a weighted directed acyclic graph (DAG), which then be accommodated with operation resources, like computing and link bandwidth, from the nodes in the ubiquitous computing environment comprised of center/edge cloud nodes and mobile devices. To solve the resource scheduling problem, which is NP-hard, we propose a reinforcement learning-based method to minimize the execution time of the service. The simulation results show that the proposed approach outperforms other baselines on saving execution time and reduce resource occupation at the same time.

The problem of real-time scheduling based on the Directed Acyclic Graph (DAG) task model has been extensively studied in the literature. Most of the studies are aimed at the development of efficient scheduling algorithms to reduce deadline misses and/or improve schedulability bounds on the given task system. In order to guarantee real-time performance of the DAG task model in practice, the latency imposed on the communication between the DAG nodes must be systematically taken into account. This paper aims at demystifying the latency of the Robot Operating System (ROS) as a practical DAG task model, which leverages the publish/subscribe mechanism to send and receive data between the nodes. To this end we present the ROS-Aware Publish/Subscribe Latency Evaluation Tool (RAPLET) which is designed to measure and visualize the details of the publish/subscribe latency in ROS. RAPLET consists of (i) the LD PRELOAD scheme that inserts function hooks in user-land and (ii) the extended Berkeley Packet Filter (eBPF) scheme that monitors the run-queue level and the network states in kernel-land. The performance analysis on ROS applications, including a real-world autonomous driving software, is performed using RAPLET to demonstrate that the publish/subscribe latency imposed on inter-node communication can be demystified and reasoned with respect to system issues including the message size and network bandwidth consumption.

Apache Spark is a distributed computing framework for fast in-memory data analysis, Machine Learning jobs, and SQL queries that employs Resilient Distributed Dataset for distributing data and Directed Acyclic Graph for scheduling computations. Currently, Spark provides two scheduling policies for tasks pending execution: a First In First Out policy and a FAIR policy, providing no support for deadline-based real-time scheduling. In this paper, we present a new system designed to accept deadlines for heterogeneous Spark Jobs and perform a real-time scheduling policy based on Earliest Deadline First (EDF). To showcase the efficiency of our scheduling policy, we compare and analyze the performance of our solution with the current Spark execution policies in terms of job lateness. We empirically prove that our real-time policy provides much lower lateness given suitable constraints.

International audience; IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections.

Data and rules power expert systems and intelligent systems. Rules, as a form of knowledge representation, can be acquired by experts or learned from data. The accuracy and precision of knowledge largely determines the success of the systems, which awakens the concern for causality. The ability to elicit cause–effect rules directly from data is key and difficult to any expert systems and intelligent systems. Rough set theory has succeeded in automatically transforming data into knowledge, where data are often presented as an attribute-value table. However, the existing tools in this theory are currently incapable of interpreting counterfactuals and interventions involved in causal analysis. This paper offers an attempt to characterize the cause–effect relationships between attributes in attribute-value tables with intent to overcome existing limitations. First, we establish the main conditions that attributes need to satisfy in order to estimate the causal effects between them, by employing the back-door criterion and the adjustment formula for a directed acyclic graph. In particular, based on the notion of lower approximation, we extend the back-door criterion to an original data table without any graphical structures. We then identify the effects of the interventions and the counterfactual interpretation of causation between attributes in such tables. Through illustrative studies completed for some attribute-value tables, we show the procedure for identifying the causation between attributes and examine whether the dependency of the attributes can describe causality between them.

Many binary code analysis tools rely on intermediate representation (IR) derived from a binary code, instead of working directly with machine instructions. In this paper, we first consider binary code analysis problems that benefit from IR and compile a list of requirements that the IR suitable for solving these problems should meet. Generally speaking, a universal binary analysis platform requires two principal components. The first component is a retargetable instruction decoder that utilizes external specifications to describe target instruction sets. External specifications facilitate maintenance and allow one to quickly implement support for new instruction sets. We analyze some of the most popular instruction set architectures (ISAs), including those used in microcontrollers, and from that compile a list of requirements for the retargetable decoder. We then overview existing multi-ISA decoders and propose our vision of a more generic approach, based on a multi-layer directed acyclic graph that describes the decoding process in universal terms. The second component of the analysis platform is the actual architecture-neutral IR. In this paper, we describe such IRs and propose Pivot 2, an IR that is low-level enough to be easily constructed from decoded machine instructions, also being easy to analyze. The main features of Pivot 2 are explicit side effects, SSA variables, simpler alternative to phi-functions, and extensible elementary operation set at the core. This IR also supports machines that have multiple memory address spaces. Finally, we propose a way to tie the decoder and the IR together to fit them to most of the binary code analysis tasks through abstract interpretation on top of the IR. The proposed scheme takes into account various aspects of target architectures that are overlooked in many other works, including pipeline specifics (handling of delay slots, hardware loop support, etc.), exception and interrupt management, and generic address space model, in which accesses may have arbitrary side effects due to memory-mapped devices or other non-trivial behavior of the memory system.

SummaryIn this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research.

Privacy is one of the biggest concerns that hinder most organizations to adopt the Big Data technology. Some mechanisms and systems have been set-up to handle huge databases. Nevertheless, the scalability requirements of Big Data are far beyond the conventional databases to handle. Therefore, it is trivial to set-up scalable privacy algorithms for conventional databases. Most data are stored in a single location, which means the records it keeps are open and effortlessly irrefutable to third parties. Centralized versions of this data make it too easy for hackers to attack. As such, in this paper, we present the opportunities and challenges of implementing cryptography and blockchain for privacy perseverance in Big Data, focusing in the healthcare domain. In addition, we also present some use cases of integrating Directed Acyclic Graph (DAG) into healthcare database framework for anchoring information security and privacy.

This paper presents a mathematical model for a mechanical computer called the Turing Tumble. We show that our model called Turing Tumble Model (TTM) is computationally universal under the assumptions that a configuration of TTM is sufficiently large and that local interactions between elements can be transferred without limitations. The Turing Tumble has a strict constraint, based on gravity, since signals can only move from top to bottom. We introduce a uniform scheme that takes into account this restriction in directionality to construct universal machines in the TTM based on directed acyclic graphs. This model may be useful for implementing computers that exploit mechanical interactions in nature, especially those on micrometer-scales.

High-performance graph query systems are a scalable way to mine information in Knowledge Graphs, especially when the queries benefit from a high-level expressive query language. This paper presents techniques to algorithmically compile queries expressed in a high-level language (e.g., Datalog) into a directed acyclic graph query plan and details how these queries can be run on a Pregel graph vertex-centric compute system. Our solution, called PathQuery Pregel, creates plans for any conjunctive or disjunctive queries with aggregation and negation; we describe how the query execution extracts graph results optimally while avoiding many join operations where parallel map execution is permitted. We provide details of how we scaled this system out to execute large set of queries in parallel over the Google Knowledge Graph, a graph of 70B edges, or facts; we describe our production experience with PathQuery Pregel.

The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.

Web service composition is considered as the hottest and potential research area in the domain of Service Oriented Architecture since the users focus on Quality of Service (QoS) and transaction properties included in the integration of services. Moreover, the potential quality of modularity and reusability features of web services has wide open the feasible options of integrating diversified function oriented services together with the better optimization capability. Hence, a meta-heuristic approach-based web service composition scheme is essential for facilitating superior and comprehensive quality during the process of integrating services. In this paper, An Integrated Probability Multi-search and Solution Acceptance Rule-based Artificial Bee Colony Optimization Scheme (IPM-SAR-ABCOS) is proposed for optimizing the process of service compositions derived using transaction and QoS characteristics of services. This proposed IPM-SAR-ABCOS is efficient in determining the optimal path that exists between the source and sink vertex of the workflow inspired directed acyclic graph that aids in predominant service composition. The proposed IPM-SAR-ABCOS uses the rules of acceptance and multi-search probabilistic parameter for addressing the process of global optimization in service composition. The experimental analysis of the proposed IPM-SAR-ABCOS inferred that its response time, accuracy and recall value is enhanced by 24%, 22% and 19% excellent to the ABC-based meta-heuristic service composition techniques considered for analysis.

Biological regulatory pathways provide important information for target gene cancer therapy. Frequently, estimating the gene networks of two distinct patient groups is a worthwhile investigation. T...

In this article, the authors have proposed an integrated algorithm for instruction scheduling and register allocation, and implemented it for compiler optimization in a machine description in a trimaran infrastructure for exploitation of instruction-level parallelism. For this experimental work, the authors added machine descriptions (MDES) targeted to HL-PD architecture. As a result, only a few spills were needed and the quality of the code generated was improved. The application of abstract base graph has been applied for experimental purposes. For these experiments, this article used 20 benchmarks available with trimaran infrastructure for HPL-PD architecture. This article compares some of these results with results obtained performed on LLVM compiler on an MIPS architecture. The implemented algorithm is based on subgraph isomorphism. The input program is represented in the form of directed acyclic graph (DAG). The vertices of the DAG represent the instructions, input, and output operands of the program, while the edges represent dependencies among the instructions.